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Author (up) Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; B. Cardenas; G. Fonseka; E. Anton; Alvaro Pascual; Richard Frodsham; Zaida Sarrate
Title Time to match; when do homologous chromosomes become closer? Type Journal Article
Year 2022 Publication Chromosoma Abbreviated Journal CHRO
Volume Issue Pages
Keywords
Abstract In most eukaryotes, pairing of homologous chromosomes is an essential feature of meiosis that ensures homologous recombination and segregation. However, when the pairing process begins, it is still under investigation. Contrasting data exists in Mus musculus, since both leptotene DSB-dependent and preleptotene DSB-independent mechanisms have been described. To unravel this contention, we examined homologous pairing in pre-meiotic and meiotic Mus musculus cells using a threedimensional fuorescence in situ hybridization-based protocol, which enables the analysis of the entire karyotype using DNA painting probes. Our data establishes in an unambiguously manner that 73.83% of homologous chromosomes are already paired at premeiotic stages (spermatogonia-early preleptotene spermatocytes). The percentage of paired homologous chromosomes increases to 84.60% at mid-preleptotene-zygotene stage, reaching 100% at pachytene stage. Importantly, our results demonstrate a high percentage of homologous pairing observed before the onset of meiosis; this pairing does not occur randomly, as the percentage was higher than that observed in somatic cells (19.47%) and between nonhomologous chromosomes (41.1%). Finally, we have also observed that premeiotic homologous pairing is asynchronous and independent of the chromosome size, GC content, or presence of NOR regions.
Address August, 2022
Corporate Author Thesis
Publisher Place of Publication Editor
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ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 601.139; 600.145; 600.096 Approved no
Call Number Admin @ si @ SBG2022 Serial 3719
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Author (up) Mirko Arnold; Anarta Ghosh; Gerard Lacey; Stephen Patchett; Hugh Mulcahy
Title Indistinct frame detection in colonoscopy videos Type Conference Article
Year 2009 Publication Machine Vision and Image Processing Conference Abbreviated Journal
Volume Issue Pages 47-52
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference
Notes MV Approved no
Call Number fernando @ fernando @ Serial 2424
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Author (up) Mirko Arnold; Anarta Ghosh; Glen Doherty; Hugh Mulcahy; Stephen Patchett; Gerard Lacey
Title Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy Type Conference Article
Year 2013 Publication Proceedings of the International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 48-53
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area 800 Expedition Conference VISIGRAPP
Notes MV Approved no
Call Number fernando @ fernando @ Serial 2427
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Author (up) Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey
Title Automatic segmentation and inpainting of specular highlights for endoscopic imaging Type Journal Article
Year 2010 Publication EURASIP Journal on Image and Video Processing Abbreviated Journal EURASIP JIVP
Volume 2010 Issue 9 Pages
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Abstract
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area 800 Expedition Conference
Notes MV Approved no
Call Number fernando @ fernando @ Serial 2423
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Author (up) Mirko Arnold; Stephan Ameling; Anarta Ghosh; Gerard Lacey
Title Quality Improvement of Endoscopy Videos Type Conference Article
Year 2011 Publication Proceedings of the 8th IASTED International Conference on Biomedical Engineering Abbreviated Journal
Volume 723 Issue Pages
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area 800 Expedition Conference
Notes MV Approved no
Call Number fernando @ fernando @ Serial 2426
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Author (up) Misael Rosales
Title Empirical Simulation Moldel of Intravascular Ultrasound Type Miscellaneous
Year 2002 Publication Director: P. Radeva, Master Thesis. Abbreviated Journal
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Address CVC (UAB)
Corporate Author Thesis
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Notes Approved no
Call Number Admin @ si @ Ros2002 Serial 323
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Author (up) Misael Rosales
Title A Physics-Based Image Modelling of IVUS as a Geometric and Kinematic System Type Book Whole
Year 2005 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address CVC (UAB)
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Petia Radeva
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition 978-84-922529-8-7 Conference
Notes Approved no
Call Number Admin @ si @ Ros2005 Serial 603
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Author (up) Misael Rosales; Petia Radeva
Title Empirical simulation model of intravascular ultrasound Type Report
Year 2003 Publication CVC Technical Report #71 Abbreviated Journal
Volume Issue Pages
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Abstract
Address CVC (UAB)
Corporate Author Thesis
Publisher Place of Publication Editor
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Area Expedition Conference
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RoR2003 Serial 520
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Author (up) Misael Rosales; Petia Radeva; J. Mauri; Oriol Pujol
Title Simulation Model of Intravascular Ultrasound Images Type Miscellaneous
Year 2004 Publication MICCAI, 2004, Saint Melo, France Abbreviated Journal
Volume Issue Pages
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Abstract
Address Springer Verlag
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ RRM2004b Serial 464
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Author (up) Misael Rosales; Petia Radeva; Oriol Rodriguez-Leor; Debora Gil
Title Modelling of image-catheter motion for 3-D IVUS Type Journal Article
Year 2009 Publication Medical image analysis Abbreviated Journal MIA
Volume 13 Issue 1 Pages 91-104
Keywords Intravascular ultrasound (IVUS); Motion estimation; Motion decomposition; Fourier
Abstract Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS.
Address
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Notes IAM;MILAB Approved no
Call Number IAM @ iam @ RRR2009 Serial 1646
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Author (up) Misael Rosales; Petia Radeva; Oriol Rodriguez; Debora Gil
Title Suppression of IVUS Image Rotation. A Kinematic Approach Type Book Chapter
Year 2005 Publication Functional Imaging and Modeling of the Heart Abbreviated Journal LNCS
Volume 3504 Issue Pages 889-892
Keywords
Abstract IntraVascular Ultrasound (IVUS) is an exploratory technique used in interventional procedures that shows cross section images of arteries and provides qualitative information about the causes and severity of the arterial lumen narrowing. Cross section analysis as well as visualization of plaque extension in a vessel segment during the catheter imaging pullback are the technique main advantages. However, IVUS sequence exhibits a periodic rotation artifact that makes difficult the longitudinal lesion inspection and hinders any segmentation algorithm. In this paper we propose a new kinematic method to estimate and remove the image rotation of IVUS images sequences. Results on several IVUS sequences show good results and prompt some of the clinical applications to vessel dynamics study, and relation to vessel pathology.
Address
Corporate Author Thesis
Publisher Springer Berlin / Heidelberg Place of Publication Editor Frangi, Alejandro and Radeva, Petia and Santos, Andres and Hernandez, Monica
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume 3504 Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;MILAB Approved no
Call Number IAM @ iam @ RRR2005 Serial 1645
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Author (up) Mohamed Ali Souibgui
Title Document Image Enhancement and Recognition in Low Resource Scenarios: Application to Ciphers and Handwritten Text Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this thesis, we propose different contributions with the goal of enhancing and recognizing historical handwritten document images, especially the ones with rare scripts, such as cipher documents.
In the first part, some effective end-to-end models for Document Image Enhancement (DIE) using deep learning models were presented. First, Generative Adversarial Networks (cGAN) for different tasks (document clean-up, binarization, deblurring, and watermark removal) were explored. Next, we further improve the results by recovering the degraded document images into a clean and readable form by integrating a text recognizer into the cGAN model to promote the generated document image to be more readable. Afterward, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion.
The second part of the thesis addresses Handwritten Text Recognition (HTR) in low resource scenarios, i.e. when only few labeled training data is available. We propose novel methods for recognizing ciphers with rare scripts. First, a few-shot object detection based method was proposed. Then, we incorporate a progressive learning strategy that automatically assignspseudo-labels to a set of unlabeled data to reduce the human labor of annotating few pages while maintaining the good performance of the model. Secondly, a data generation technique based on Bayesian Program Learning (BPL) is proposed to overcome the lack of data in such rare scripts. Thirdly, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE). This latter self-supervised model is designed to tackle two tasks, text recognition and document image enhancement. The proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time, it requires substantially fewer data samples to converge.
In the third part of the thesis, we analyze, from the user perspective, the usage of HTR systems in low resource scenarios. This contrasts with the usual research on HTR, which often focuses on technical aspects only and rarely devotes efforts on implementing software tools for scholars in Humanities.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Alicia Fornes;Yousri Kessentini
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-8-6 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ Sou2022 Serial 3757
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Author (up) Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados
Title One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition Type Conference Article
Year 2022 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords Document Analysis
Abstract Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data.
Address Virtual; January 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference WACV
Notes DAG; 602.230; 600.140 Approved no
Call Number Admin @ si @ SBD2022 Serial 3615
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Author (up) Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor
Title A Few-shot Learning Approach for Historical Encoded Manuscript Recognition Type Conference Article
Year 2021 Publication 25th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 5413-5420
Keywords
Abstract Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.
Address Virtual; January 2021
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes DAG; 600.121; 600.140 Approved no
Call Number Admin @ si @ SFK2021 Serial 3449
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Author (up) Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi
Title Few shots are all you need: A progressive learning approach for low resource handwritten text recognition Type Journal Article
Year 2022 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 160 Issue Pages 43-49
Keywords
Abstract Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.121; 600.162; 602.230 Approved no
Call Number Admin @ si @ SFK2022 Serial 3736
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